1. Field of the Invention
The present invention relates generally to context-aware applications for computer systems, and more particularly to a novel context fusing system and methodology for supporting context-aware devices and applications.
2. Description of the Prior Art
Pervasive computing applications are increasingly relying on the context of the user whom the application is designed to support. Contextual information is derived from a variety of sources and enables the application to provide the user with qualitatively better service. For instance, context awareness can be seen as key to such pervasive computing features as personalized access, intention inference, resource opportunism, and system proactivity. The net result of improved context awareness is a reduced demand for human attention by the application.
Context awareness is made possible by the ubiquity of sensors of different kinds. For instance, several sources of location information are typically available—consider the rapidly expanding use of cellular phones (some of which are already enabled for location tracking in order to help emergency rescue teams), GPS receivers, wireless LAN computers, wireless PDAs such as the Blackberry device, electronic calendars, and active badges. Despite this abundance of contextual information, one key problem remains: resolution of conflicts and ambiguities among different information sources.
As a motivating example, the following scenario is considered: A user's car is equipped with a GPS tracking device which periodically beacons the car's location to a central server. The user also carries a wireless PDA which beacons its location to the same server. Now, if the car was last reported seen in “San Francisco” 5 minutes ago and the PDA was last reported seen in “San Jose” 10 minutes ago, which location report should the system pick as the most current and accurate? In other words, which context is the user currently in? Surely the user cannot have moved from San Jose to San Francisco in 5 minutes.
In Paul Castro and Richard Muntz, “Managing Context Data for Smart Spaces,” IEEE Personal Communications, Vol. 7, No. 5, (October 2000), pp. 44-46, a context fusion service that uses evidential reasoning techniques, is described. However, this service has a number of disadvantages. First, it works with a pre-determined set of context sources only and does not support dynamically added sources. Second, it requires a separate training phase in order to obtain the probability distribution of input context values. Third, both input and output context values must be quantized into discrete values.
Asim Smailagic and David Kogan, “Location Sensing and Privacy in a Context Aware Computing Environment,” IEEE Wireless Communications, October 2002, teaches a method for fusing location data in a wireless LAN environment based on a combination of triangulation and mapping. The method also requires a pre-determination of context (location) sources and a separate training phase. In addition, it handles homogeneous location data only.
Another method for fusing location data is described in Jussi Myllymaki and Stefan Edlund, “Location Aggregation from Multiple Sources,” Proceedings of International Conference on Mobile Data Management, Singapore, 2002. The method ranks location reports from different sources and selects the most probable one based on factors such as time, user associativity and device mobility. Besides being location-specific, this method does not consider the relationships between input values and is not able to improve the precision of input values.
Therefore, a need exists for a general fusion technique that can accurately fuse context data from a dynamic set of sources.
It would be highly desirable to provide a context fusion system and software architecture for supporting pervasive computing devices.